Computer, bottleneck identification method, and non-transitory computer readable storage medium

ABSTRACT

A computer, which is configured to manage a resource for which a metric being an indicator for evaluating performance of the resource is to be measured, the computer being coupled to, via the interface, a management target system including a plurality of resources, and storing correlation information for managing a correlation coefficient indicating a degree of correlation between metrics, and the computer being configured to: detect a trigger event to identify a bottleneck based on a metric value of a monitored metric of a monitored resource; identify a related resource having a coupling relationship with the monitored resource; identify a correlation metric that is highly correlated with the monitored metric from among metrics of the related resource based on the correlation information; identify a combination of the related resource and the correlation metric as a bottleneck candidate.

CLAIM OF PRIORITY

The present application claims priority from Japanese patent applicationJP 2017-218981 filed on Nov. 14, 2017, the content of which is herebyincorporated by reference into this application.

BACKGROUND OF THE INVENTION

This invention relates to a computer, method, and non-transitorycomputer readable storage medium for identifying a bottleneck of asystem to be monitored.

In a computer system, for example, a data center, a virtualizationtechnology and other technologies are used to construct a system forimplementing a predetermined service. In such a computer system, thereare a large number of resources to be monitored and metrics to bemeasured from those resources, resulting in an extreme difficulty inidentifying the bottleneck.

As a technology for solving the above-mentioned problem, there is knowna technology described in JP 2011-146074 A. In JP 2011-146074 A, thereis a description of “an operation management apparatus including: acorrelation model generation module configured to generate, based ontime-series performance information indicating a chronological change inperformance information, a correlation model including a plurality ofcorrelation functions between pieces of performance information andweight information indicating prediction errors of those respectivecorrelation functions; and a model search module configured to predict,when there are a plurality of paths, which are each a correlationfunction that may predict second performance information based on firstperformance information among the pieces of performance information or acombination of correlation functions, within the correlation model, thesecond performance information by using a path whose value of weightinformation is the maximum.”

SUMMARY OF THE INVENTION

It is possible to identify the bottleneck while reducing a burden on anadministrator by using JP 2011-146074 A. However, in a case where thereare a large number of resources and metrics, that is, in a case wherethere are a large number of factors to be analyzed, there is a problemin that an extremely large amount of time is required to identify thebottleneck.

This invention has an object to provide a system and a method capable ofquickly identifying a bottleneck.

The present invention can be appreciated by the description whichfollows in conjunction with the following figures, wherein: a computer,which is configured to manage a resource for which a metric being anindicator for evaluating performance of the resource is to be measured,comprises a processor, a storage apparatus coupled to the processor, andan interface coupled to the processor. The computer is coupled to, viathe interface, a management target system including a plurality ofresources. The storage apparatus stores correlation information formanaging a correlation coefficient indicating a degree of correlationbetween metrics. The computer being configured to: detect a triggerevent to identify a bottleneck based on a metric value of a monitoredmetric of a monitored resource; identify at least one related resourcehaving a coupling relationship with the monitored resource; identify atleast one correlation metric that is highly correlated with themonitored metric from among metrics of the at least one related resourcebased on the correlation information; identify a combination of the atleast one related resource and the at least one correlation metric as abottleneck candidate; and generate notification information fornotifying of the bottleneck candidate, and output the notificationinformation.

According to one embodiment of this invention, the computer can quicklyidentify the bottleneck (bottleneck candidate). Problems,configurations, and effects other than described above will becomeapparent from a description of an embodiment below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention can be appreciated by the description whichfollows in conjunction with the following figures, wherein:

FIG. 1 is a diagram for illustrating an exemplary configuration of acomputer system in a first embodiment;

FIG. 2 is a diagram for illustrating an exemplary configuration of abusiness operation system constructed in a management target system inthe first embodiment;

FIG. 3 is a table for showing an example of a data structure ofconfiguration information in the first embodiment;

FIG. 4 is a table for showing an example of a data structure ofresource-related information in the first embodiment;

FIG. 5 is a table for showing an example of a data structure of metriccorrelation information in the first embodiment;

FIG. 6 is a table for showing an example of a data structure ofcorrelation coefficient information in the first embodiment;

FIG. 7 is a table for showing an example of a data structure of metricvalue history information in the first embodiment;

FIG. 8 is a table for showing an example of a data structure of metricconversion information in the first embodiment;

FIG. 9 is a table for showing an example of a data structure ofconversion function information in the first embodiment;

FIG. 10 is a flowchart for illustrating an outline of processing to beexecuted in a case where a management server in the first embodiment hasdetected a trigger event to identify a bottleneck;

FIG. 11 is a flowchart for illustrating related-resource identificationprocessing to be executed by the management server in the firstembodiment;

FIG. 12 is a flowchart for illustrating high correlation metricidentification processing to be executed by the management server in thefirst embodiment;

FIG. 13 is a flowchart for illustrating bottleneck candidateidentification processing to be executed by the management server in thefirst embodiment;

FIG. 14 is a flowchart for illustrating estimated metric valuecalculation processing to be executed by the management server in thefirst embodiment;

FIG. 15 is a diagram for illustrating an example of an operation screento be displayed on a client terminal in the first embodiment; and

FIG. 16 is a flowchart for illustrating an example of processing ofupdating the metric correlation information and the correlationcoefficient information to be executed by the management server in thefirst embodiment.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Now, a description is given of an embodiment of this invention referringto the drawings. It should be noted that this invention is not to beconstrued by limiting the invention to the content described in thefollowing embodiment. A person skilled in the art would easily recognizethat a specific configuration described in the following embodiment maybe changed within the scope of the concept and the gist of thisinvention.

In a configuration of this invention described below, the same orsimilar components or functions are assigned with the same referencenumerals, and a redundant description thereof is omitted here.

Notations of, for example, “first”, “second”, and “third” herein areassigned to distinguish between components, and do not necessarily limitthe number or order of those components.

The position, size, shape, range, and others of each componentillustrated in, for example, the drawings may not represent the actualposition, size, shape, range, and other metrics in order to facilitateunderstanding of this invention. Thus, this invention is not limited tothe position, size, shape, range, and others described in, for example,the drawings.

First Embodiment

FIG. 1 is a diagram for illustrating an exemplary configuration of acomputer system in a first embodiment of the present invention. FIG. 2is a diagram for illustrating an exemplary configuration of a businessoperation system constructed in a management target system 103 in thefirst embodiment.

The computer system includes a management server 100, a client terminal101, and a management target system 103. The management server 100, theclient terminal 101, and the management target system 103 are coupled toone another via a network 105. The network 105 is, for example, a localarea network (LAN) or a wide area network (WAN). The coupling may beimplemented in a wired or wireless manner.

The management target system 103 is a system including a plurality ofapparatus. A business operation system for implementing a predeterminedservice is constructed on the management target system 103. The businessoperation system includes a physical and virtual group of resources.

As illustrated in FIG. 2, the management target system 103 in the firstembodiment includes three computers 201-1, 201-2, and 201-3, twoswitches 202-1 and 202-2, and a storage apparatus 203. A businessoperation system including a host computer, a storage system, and astorage area network (SAN) operates in the management target system 103.

The host computer includes, as resources, an application (APP), avirtual machine (VM), a hypervisor (HV), and a data store (DS). The SANincludes the switch 202 as a resource. The storage system includes, asresources, a port, a logical device (LDEV), an MP blade (MP), a storagearea pool (pool), a physical volume group (PG), and a cache.

A value of an indicator for evaluating the performance of a resource canbe measured from the resource. In the following description, theindicator is referred to as a “metric”. Further, the above-mentionedvalue of the indicator is referred to as a “metric value”. The metricis, for example, a CPU usage, a memory usage, a response time, and delayof communication.

In the first embodiment, a combination of a resource and a metric isidentified as a bottleneck. The resource to be identified as abottleneck does not include an application.

The management server 100 is configured to manage the management targetsystem 103, and identify a bottleneck in a case where, for example, afailure has occurred or performance has deteriorated. Management of themanagement target system 103 includes, for example, construction of asystem, change of a system configuration, and monitoring of the system.

The client terminal 101 is a terminal to be used by a user who operatesthe management server 100. When the user can directly operate themanagement server 100, the computer system may not include the clientterminal 101.

Now, a description is given of a hardware configuration and softwareconfiguration of the management server 100.

The management server 100 includes a processor 111, a memory 112, and anetwork interface 113 in terms of hardware. Pieces of hardware arecoupled to one another via, for example, an internal bus. The managementserver 100 may include a storage medium such as a hard disk drive (HDD)and a solid state drive (SSD).

The processor 111 is configured to execute a program stored in thememory 112. The processor 111 operates as a functional module (module)for implementing a specific function by executing processing inaccordance with a program. In the following description, when processingis described with the functional module serving as a subject of asentence, it means that the processor 111 is executing a program forimplementing the functional module. The memory 112 stores a program tobe executed by the processor 111 and information to be used by theprogram. Further, the memory 112 includes a work area to be used by theprogram. Details of the program and information stored in the memory 112are described later.

The network interface 113 is an interface for coupling to anotherapparatus via a network.

Now, a description is given of the program and information stored in thememory 112. The memory 112 stores programs for implementing a servercontrol module 121, an analysis target determination module 122, acorrelation information generation module 123, a bottleneckidentification module 124, and a metric value estimation module 125.Further, the memory 112 stores configuration information 141,resource-related information 142, metric correlation information 143,correlation coefficient information 144, metric value historyinformation 145, metric conversion information 146, and conversionfunction information 147.

The configuration information 141 is information for managing resourcesincluded in a business operation system. Details of the configurationinformation 141 are described with reference to FIG. 3. Theresource-related information 142 is information for managing relevanceamong resources. Details of the resource-related information 142 aredescribed with reference to FIG. 4.

The metric correlation information 143 is information for managingcorrelation among metrics of resources. The correlation coefficientinformation 144 is information for managing a correlation coefficientindicating a degree of the correlation defined in the metric correlationinformation 143. Details of the metric correlation information 143 andthe correlation coefficient information 144 are described with referenceto FIG. 5 and FIG. 6.

The metric value history information 145 is information for managing ahistory of a measured metric value. Details of the metric value historyinformation 145 are described with reference to FIG. 7.

The metric conversion information 146 is information for managing amethod of converting a metric value of one resource into a metric valueof another resource. The conversion function information 147 isinformation for managing a conversion function that converts a metricvalue. Details of the metric conversion information 146 and theconversion function information 147 are described with reference to FIG.8 and FIG. 9.

The server control module 121 is configured to control the entiremanagement server 100. The analysis target determination module 122 isconfigured to determine a combination of a resource to be analyzed and ametric. The analysis target determination module 122 includes a resourcenarrowing-down module 131 and a metric narrowing-down module 132. Thecorrelation information generation module 123 is configured to generateand update the metric correlation information 143 and the correlationcoefficient information 144. The bottleneck identification module 124 isconfigured to perform analysis and identify a bottleneck based on aresult of analysis. The metric value estimation module 125 is configuredto calculate an estimation value (estimated metric value) of a metricvalue.

Details of processing to be executed by the analysis targetdetermination module 122, the correlation information generation module123, the bottleneck identification module 124, and the metric valueestimation module 125 are described later.

This concludes the description of the hardware configuration andsoftware configuration of the management server 100. Next, a descriptionis given of a hardware configuration and software configuration of theclient terminal 101.

The client terminal 101 includes a processor 151, a memory 152, anetwork interface 153, an input device 154, and an output device 155 interms of hardware. Pieces of hardware are coupled to one another via,for example, an internal bus.

The processor 151, the memory 152, and the network interface 153 arehardware components similar to the processor 111, the memory 112, andthe network interface 113. The input device 154 is a device forreceiving input of data. The input device 154 is, for example, akeyboard, a mouse, or a touch panel. The output device 155 is a devicefor outputting data. The output device 155 is, for example, a touchpanel or a display. The network interface 113 may be used as the outputdevice 155.

The memory 152 stores a client control module 161 configured to controlthe entire client terminal 101.

Regarding the functional modules of each of the management server 100and the client terminal 101, a plurality of functional modules may beintegrated into one functional module, or one functional module may bedivided into a plurality of functional modules.

FIG. 3 is a table for showing an example of a data structure of theconfiguration information 141 in the first embodiment.

The configuration information 141 includes an entry formed of a resourcename 301 and a component type 302. One entry corresponds to one resourceincluded in the business operation system.

The resource name 301 is a field that stores an identification name foruniquely identifying a resource included in the business operationsystem. The component type 302 is a field that stores information foridentifying the type of a resource such as a hardware resource or asoftware resource that implements the resource.

In the first embodiment, the management server 100 is configured toperiodically measure metrics of a plurality of resources that aremanaged by using the configuration information 141. The measured metricvalues are stored into the metric value history information 145.

FIG. 4 is a table for showing an example of a data structure of theresource-related information 142 in the first embodiment.

The resource-related information 142 includes an entry formed of aresource name 401 and a used resource name 402. There is one entry forone combination of a resource and a used resource.

The resource name 401 is the same field as the resource name 301. Theused resource name 402 is a field that stores an identification name ofa resource that is associated with a resource corresponding to theresource name 401.

In the first embodiment, a relevance (coupling relationship) betweenresources is defined in a tree structure in which the virtual machine isat the top layer and a physical volume or a cache is at the bottomlayer.

FIG. 5 is a table for showing an example of a data structure of themetric correlation information 143 in the first embodiment. FIG. 6 is atable for showing an example of a data structure of the correlationcoefficient information 144 in the first embodiment.

The metric correlation information 143 is information having a datastructure of a matrix format. There is one piece of metric correlationinformation 143 for one combination of resources. A row and a columncorrespond to metrics of respective resources.

In a case where metrics are correlated to each other, a cell identifiedby a combination of those metrics stores identification information forreading out a correlation coefficient from the correlation coefficientinformation 144. The correlation coefficient is controlled not to be setin a cell of the same metric.

There is one piece of correlation coefficient information 144 for onecombination of resources. The correlation coefficient information 144includes an entry formed of a correlation coefficient ID 601 and acorrelation coefficient 602. One entry corresponds to one correctioncoefficient.

The correlation coefficient ID 601 is a field that stores identificationinformation on a correlation coefficient. The correlation coefficient602 is a field that stores a correlation coefficient indicating a degreeof correlation between metrics. The correlation coefficient is a realnumber equal to or larger than “−1” and equal to or smaller than “1”. Asthe absolute value of the correlation coefficient becomes closer to 1,the correlation between metrics is indicated to be larger.

The management server 100 in the first embodiment holds the metriccorrelation information 143 and the correlation coefficient information144 to manage correlation between metrics, but may hold only the metriccorrelation information 143 in which a correlation coefficient is set ina cell.

FIG. 7 is a table for showing an example of a data structure of themetric value history information 145 in the first embodiment.

The metric value history information 145 includes an entry formed of aresource name 701, a metric 702, a time 703, and a metric value 704.

The resource name 701 is the same field as the resource name 301. Themetric 702 is a field that stores identification information on a metricthat can be obtained from a resource corresponding to the resource name701. The time 703 is a field that stores a measurement time. The metricvalue 704 is a field that stores the measured metric value.

FIG. 8 is a table for showing an example of a data structure of themetric conversion information 146 in the first embodiment. FIG. 9 is atable for showing an example of a data structure of the conversionfunction information 147 in the first embodiment.

The metric conversion information 146 includes an entry formed of aconversion source 801, a conversion destination 802, and a function ID803. One entry corresponds to definition information on one conversionmethod.

The conversion source 801 is a group of fields for specifying the metricof a conversion source, and includes a resource name 811 and a metric812. The conversion destination 802 is a group of fields for specifyingthe metric of a conversion destination, and includes a resource name 821and a metric 822. The function ID 803 is a field that storesidentification information on a conversion function. The conversionfunction is a function for calculating a metric value specified by theconversion destination 802 from a metric value specified by theconversion source 801.

The conversion function information 147 includes an entry formed of afunction ID 901, a function 902, and a parameter 903. One entrycorresponds to one conversion function.

The function ID 901 is the same field as the function ID 803. Thefunction 902 is a field that stores a conversion function. The parameter903 is a field that stores the value of a parameter set in theconversion function. Conversion functions can be managed easily bymanaging a function including parameters and those parameters separatelyfrom each other.

The management server 100 in the first embodiment holds the metricconversion information 146 and the conversion function information 147in order to manage the conversion function, but may hold only the metricconversion information 146 including a field that stores a conversionfunction having set parameter values instead of the function ID 803.

Next, a description is given of processing to be executed by themanagement server 100 to identify a bottleneck candidate with referenceto FIG. 10 to FIG. 14.

FIG. 10 is a flowchart for illustrating an outline of processing to beexecuted in a case where the management server 100 in the firstembodiment has detected a trigger event to identify a bottleneck.

In the first embodiment, it is assumed that a combination of a resourceand a metric, which are monitored to detect a trigger event to executeprocessing described below, is set in advance. In the followingdescription, the resource and the metric, which are monitored to detecta trigger event to execute to the processing, are referred to as“monitored resource” and “monitored metric”, respectively.

It should be noted that this invention is not limited to the triggerevent to identify a bottleneck. For example, the management server 100compares the metric value and threshold value of any resource, anddetermines whether performance deterioration has occurred based on theresult of comparison. In a case where it is determined that performancedeterioration has occurred, the management server 100 starts processingdescribed below. The management server 100 may start the processing in acase where a notification of occurrence of performance deterioration isreceived from the outside.

First, the server control module 121 of the management server 100 callsand instructs the analysis target determination module 122 to executerelated-resource identification processing for identifying a relatedresource of a monitored resource (Step S101). This processing can beexecuted to reduce the number of resources to be analyzed. Details ofthe related-resource identification processing are described withreference to FIG. 11. In this case, the related resource represents aresource to which a resource serving as a start point can be coupleddirectly or via other resources.

Next, the server control module 121 of the management server 100 callsand instructs the analysis target determination module 122 to executehigh correlation metric identification processing for identifying ametric (high correlation metric) that is highly correlated with themonitored resource from among metrics of the related resource (StepS102). This processing can be executed to reduce the number of metricsto be analyzed. Details of the high correlation metric identificationprocessing are described with reference to FIG. 12.

Next, the server control module 121 of the management server 100 callsand instructs the bottleneck identification module 124 to executebottleneck candidate identification processing (Step S103). Thisprocessing is executed to generate a bottleneck candidate list. Detailsof the bottleneck candidate identification processing are described withreference to FIG. 13.

Next, the bottleneck identification module 124 of the management server100 generates display information for presenting the bottleneckcandidate list, outputs the display information (Step S104), and thenends the processing. For example, the management server 100 transmitsthe display information to the client terminal 101.

FIG. 11 is a flowchart for illustrating the related-resourceidentification processing to be executed by the management server 100 inthe first embodiment.

The resource narrowing-down module 131 of the analysis targetdetermination module 122 sets the monitored resource as the startresource (Step S201).

Specifically, the resource narrowing-down module 131 sets a variablerepresenting the start resource to an identification name of themonitored resource. Further, the resource narrowing-down module 131initializes path information and a related-resource list.

Next, the resource narrowing-down module 131 refers to theresource-related information 142 (Step S202) to determine whether thereis a resource that can be coupled to the start resource (Step S203).

Specifically, the resource narrowing-down module 131 searches for anentry in which the identification name of the resource set as the startresource is set in the resource name 401. In a case where there is suchan entry, the resource narrowing-down module 131 determines that thereis a resource that can be coupled to the start resource.

In a case where it is determined that there is a resource that can becoupled to the start resource, the resource narrowing-down module 131updates the path information by registering the retrieved entry in thepath information (Step S204). At this time, the resource narrowing-downmodule 131 registers the resource that can be coupled to the startresource in a list of remaining related resources to be processed.

Next, the resource narrowing-down module 131 sets a remaining relatedresource to be processed as a next start resource (Step S205). Afterthat, the resource narrowing-down module 131 returns to Step S202, andexecutes similar processing. At this time, the resource narrowing-downmodule 131 deletes an identification name of the next start resourcefrom the list of remaining related resources to be processed.

In a case where it is determined that there is no resource that can becoupled to the start resource, the resource narrowing-down module 131determines whether there is a remaining related resource to be processed(Step S206).

In a case where it is determined that there is a remaining relatedresource to be processed, the resource narrowing-down module 131advances to Step S205.

In a case where it is determined that there is no remaining relatedresource to be processed, the resource narrowing-down module 131generates the related-resource list by using the path information (StepS207).

Specifically, the resource narrowing-down module 131 reads out the valueof the used resource name 402 of an entry registered in the pathinformation, and registers the value in the related-resource list. Atthis time, the resource narrowing-down module 131 performs control sothat a duplicate value is not registered in the related-resource list.The resource narrowing-down module 131 outputs the generatedrelated-resource list to the metric narrowing-down module 132.

FIG. 12 is a flowchart for illustrating the high correlation metricidentification processing to be executed by the management server 100 inthe first embodiment.

The metric narrowing-down module 132 of the analysis targetdetermination module 122 starts loop processing for a related resource(Step S301).

Specifically, the metric narrowing-down module 132 selects one relatedresource from among related resources registered in the related-resourcelist.

At this time, the metric narrowing-down module 132 initializes a highcorrelation metric list.

Next, the metric narrowing-down module 132 starts loop processing for ametric of the related resource (Step S302).

Specifically, the metric narrowing-down module 132 selects one metricfrom among metrics that can be obtained from the related resource. Inthe following description, the metric selected by the metricnarrowing-down module 132 is referred to as a “selected metric”.

Next, the metric narrowing-down module 132 refers to the metriccorrelation information 143 and the correlation coefficient information144 corresponding to a combination of the monitored resource and therelated resource to obtain a correlation coefficient for correlationbetween the monitored metric and the selected metric (Step S303).

Specifically, the metric narrowing-down module 132 refers to the metriccorrelation information 143 corresponding to a combination of themonitored resource and the related resource to obtain identificationinformation on a correlation coefficient set in a cell corresponding toa combination of the monitored metric and the selected metric. Further,the metric narrowing-down module 132 refers to the correlationcoefficient information 144 corresponding to a combination of themonitored metric and the related resource, searches for an entry inwhich the correlation coefficient ID 601 matches the identificationinformation on the obtained correlation coefficient, and obtains acorrelation coefficient stored in the correlation coefficient 602 of theretrieved entry.

Next, the metric narrowing-down module 132 determines whether thecorrelation coefficient is equal to or larger than a first thresholdvalue (Step S304). It is assumed that the first threshold value is setin advance. The first threshold value can be changed appropriately.

In a case where it is determined that the correlation coefficient issmaller than the first threshold value, the metric narrowing-down module132 advances to Step S306.

In a case where it is determined that the correlation coefficient isequal to or larger than the first threshold value, the metricnarrowing-down module 132 registers an entry formed of the relatedresource, the selected metric, and the correlation coefficient in thehigh correlation metric list (Step S305). After that, the metricnarrowing-down module 132 advances to Step S306.

In Step S306, the metric narrowing-down module 132 determines whetherthe processing is complete for all the metrics of the selected relatedresource (Step S306).

In a case where it is determined that processing is not complete for allthe metrics of the selected related resource, the metric narrowing-downmodule 132 returns to Step S302, and selects a next metric to executesimilar processing.

In a case where it is determined that the processing is complete for allthe metrics of the selected related resource, the metric narrowing-downmodule 132 determines whether the processing is complete for all therelated resources registered in the related-resource list (Step S307).

In a case where it is determined that processing is not complete for allthe related resources registered in the related-resource list, themetric narrowing-down module 132 returns to Step S301, and selects anext related resource from the related-resource list to execute similarprocessing.

In a case where it is determined that the processing is complete for allthe related resources registered in the related-resource list, themetric narrowing-down module 132 ends the processing. At this time, themetric narrowing-down module 132 outputs the high correlation metriclist to the bottleneck identification module 124.

FIG. 13 is a flowchart for illustrating the bottleneck candidateidentification processing to be executed by the management server 100 inthe first embodiment.

The bottleneck identification module 124 of the management server 100starts loop processing for a high correlation metric (Step S401).

Specifically, the bottleneck identification module 124 selects onecombination of the resource and the metric from the high correlationmetric list. At this time, the bottleneck identification module 124initializes the bottleneck candidate list. In the following description,the resource and the metric selected by the bottleneck identificationmodule 124 are referred to as a “target resource” and a “target metric”,respectively.

Next, the bottleneck identification module 124 instructs the metricvalue estimation module 125 to execute estimated metric valuecalculation processing (Step S402). Details of the estimated metricvalue calculation processing are described with reference to FIG. 14. Inthe estimated metric value calculation processing, time-series data onthe estimated metric value of the monitored metric is calculated.

The bottleneck identification module 124 is in a standby state until thetime-series data on the estimated metric value of the monitored metricis output from the metric value estimation module 125.

In a case where the estimated metric value of the monitored metric isoutput from the metric value estimation module 125, the bottleneckidentification module 124 executes correlation analysis processing (StepS403).

Specifically, the bottleneck identification module 124 obtains thetime-series data on the metric value of the monitored metric from themetric value history information 145. The bottleneck identificationmodule 124 uses the time-series data on the metric value of themonitored metric and the time-series data on the estimated metric valueof the monitored metric to calculate a correlation coefficient forcorrelation between those two metrics. Further, the bottleneckidentification module 124 refers to the metric correlation information143 and the correlation coefficient information 144 to obtain acorrelation coefficient for correlation between the target metric andthe monitored metric, and calculates an error between the calculatedcorrelation coefficient and the obtained correlation coefficient.

The correlation coefficient is used as an indicator for evaluating thedegree of correlation between the high correlation metric and themonitored metric at a time when performance deterioration has occurred.The error between correlation coefficients is used as an indicator forevaluating an abnormality of the correlation at a time when performancedeterioration has occurred, for example.

In a case where the high correlation metric and the monitored metric arehighly correlated to each other, the high correlation metric is highlylikely to influence the monitored metric. In other words, the highcorrelation metric of the related resource is highly likely to be abottleneck. Further, in a case where the error between correlationcoefficients is large, it indicates that the correlation is destroyeddue to, for example, performance deterioration, and thus the highcorrelation metric is highly likely to influence the monitored metric.In other words, the high correlation metric of the related resource ishighly likely to be a bottleneck.

The bottleneck identification module 124 determines whether thecalculated correlation coefficient is equal to or larger than the secondthreshold value (Step S404).

In a case where it is determined that the calculated correlationcoefficient is smaller than the second threshold value, the bottleneckidentification module 124 determines whether the obtained correlationcoefficient is equal to or larger than a third threshold value and theerror between calculated correlation coefficients is equal to or largerthan a fourth threshold value (Step S405).

In a case where it is determined that the condition of Step S405 is notsatisfied, the bottleneck identification module 124 advances to StepS407.

In a case where it is determined that the condition of Step S405 issatisfied, the bottleneck identification module 124 advances to StepS406.

In Step S404, in a case where it is determined that the calculatedcorrelation coefficient is equal to or larger than the second thresholdvalue, the bottleneck identification module 124 advances to Step S406.

In Step S406, the bottleneck identification module 124 registers acombination of the target resource and the target metric in thebottleneck candidate list as a bottleneck candidate (Step S406). Afterthat, the bottleneck identification module 124 advances to Step S407.

Specifically, the bottleneck identification module 124 registers anentry formed of the target resource and the target metric in thebottleneck candidate list. An entry formed of the target resource, thetarget metric, and the calculated correlation coefficient may beregistered in the bottleneck candidate list.

In Step S407, the bottleneck identification module 124 determineswhether the processing is complete for all the combinations of theresource and the metric registered in the high correlation metric list.

In a case where it is determined that the processing is not complete forall the combinations of the resource and the metric registered in thehigh correlation metric list, the bottleneck identification module 124returns to Step S401, and selects a next combination to execute similarprocessing.

In a case where it is determined that the processing is complete for allthe combinations of the resource and the metric registered in the highcorrelation metric list, the bottleneck identification module 124 endsthe processing.

It should be noted that the correlation analysis is an example ofstatistical analysis processing, and is not limited thereto. Forexample, in Step S403, the bottleneck identification module 124calculates a sum of differences between pieces of the time-series dataon the metric value of the monitored metric and pieces of thetime-series data on the estimated metric value of the monitored metric,and determines whether the sum is equal to or larger than a thresholdvalue in Step S404. In a case where the sum is smaller than thethreshold value, the bottleneck identification module 124 advances toStep S406, whereas in a case where the sum is equal to or larger thanthe threshold value, the bottleneck identification module 124 advancesto Step S407.

The bottleneck identification module 124 may output the high correlationmetric list as the bottleneck candidate list without executing theprocessing of from Step S402 to Step S405. For example, in a case wherethe number of high correlation metrics registered in the highcorrelation metric list is smaller than a threshold value, such anoperation as described above can be performed to present the bottleneckcandidates quickly.

In Step S401, in a case where the bottleneck identification module 124obtains the time-series data on the metric value of the target metricand the temporal change of the metric value is small, the bottleneckidentification module 124 may advance to Step S407 without executing theprocessing of from Step S402 to Step S406 for the target metric. Thetemporal change of the metric value can be determined based on anindicator indicating the temporal change of the metric, for example, amoving average of the metric. In a case where the change in time-seriesdata at the time of occurrence of a failure is small, the metric can beestimated to have nothing to do with the failure. Therefore, such anoperation as described above can be performed to reduce the number ofmetrics to be analyzed, and thus it is possible to speed up thebottleneck identification processing.

FIG. 14 is a flowchart for illustrating the estimated metric valuecalculation processing to be executed by the management server 100 inthe first embodiment.

The metric value estimation module 125 of the management server 100obtains time-series data on the metric value of a high correlationmetric from the metric value history information 145 (Step S501).

Specifically, the metric value estimation module 125 searches for anentry in which a combination of the resource name 701 and the metric 702matches a combination of the related resource and the selected metric.Further, the metric value estimation module 125 refers to the time 703of the retrieved entry, and reads out the value of the metric value 704when the time 703 of that entry is included in a predetermined timerange. In other words, time-series data on the metric value is read out.The time range is set in advance.

Next, the metric value estimation module 125 uses the read time-seriesdata on the metric value to calculate time-series data on the estimatedmetric value of the monitored metric (Step S502). After that, the metricvalue estimation module 125 ends the processing. Specifically, thefollowing processing is executed.

The metric value estimation module 125 refers to the metric conversioninformation 146 to search for an entry in which a combination of theresource name 811 and the metric 812 of the conversion source 801matches a combination of the target resource and the target metric, anda combination of the resource name 821 and the metric 822 of theconversion destination 802 matches a combination of the monitoredresource and the monitored metric. The metric value estimation module125 obtains identification information on the retrieved entry from thefunction ID 803 thereof.

The metric value estimation module 125 refers to the conversion functioninformation 147 to search for an entry in which the function ID 901matches the obtained identification information. The metric valueestimation module 125 calculates time-series data on the estimatedmetric value of the monitored metric based on information on aconversion function stored in the retrieved entry and the time-seriesdata on the metric value of the high correlation metric. This concludesthe description of the processing of Step S502.

FIG. 15 is a diagram for illustrating an example of an operation screento be displayed on the client terminal 101 in the first embodiment.

An operation screen 1500 is a screen for setting information required toidentify a bottleneck and for displaying the bottleneck candidate list.The operation screen 1500 includes a monitored resource display field1510, a monitored metric display field 1511, threshold value settingfields 1512 and 1513, a period setting field 1514, a target metricselection field 1515, a target resource selection field 1516, athreshold value setting field 1517, a related-resource list displayfield 1520, a bottleneck candidate list display field 1530, and a graphdisplay field 1540.

The monitored resource display field 1510 is a field for displaying amonitored resource. The monitored metric display field 1511 is a fieldfor displaying a monitored metric.

The threshold value setting field 1512 is a field for setting the secondthreshold value. The threshold value setting field 1513 is a field forsetting the third threshold value.

The period setting field 1514 is a field for setting a time width of thetime-series data to be obtained at the time of calculation of theestimated metric value.

The target metric selection field 1515 and the target resource selectionfield 1516 are fields for specifying a metric and a resource forprocessing. The target metric selection field 1515 includes radiobuttons for selecting whether all or a part of metrics identified as thehigh correlation metric are selected for processing. The target resourceselection field 1516 includes radio buttons for selecting a relatedresource for processing in a case where the high correlation metric isidentified.

The threshold value setting field 1517 is a field for setting the firstthreshold value. In the first embodiment, when a radio button of “narrowdown” of the target resource selection field 1516 is operated, a valuecan be input to the threshold value setting field 1517.

The related-resource list display field 1520 is a field for displayingthe related-resource list. Identification names of resources aredisplayed in a list format on the related-resource list display field1520.

The bottleneck candidate list display field 1530 is a field fordisplaying a bottleneck candidate list. The bottleneck candidate listdisplay field 1530 includes one or more entries each formed of aselection button 1531, a metric 1533, and a correlation coefficient1534. The resource name 1532 and the metric 1533 are the same fields asthe resource name 301 and the metric 702. The correlation coefficient1534 is a field that stores a correlation coefficient calculated in StepS403. The selection button 1531 is a button for selecting a metric valueto be displayed on the graph display field 1540.

The graph display field 1540 is a field for displaying pieces oftime-series data on the metric value and estimated metric value of themonitored metric corresponding to an entry for which the selectionbutton 1531 is operated. In FIG. 15, the solid line indicatestime-series data on the metric value read out from the metric valuehistory information 145, and the broken line indicates time-series dataon the estimated metric value.

Next, a description is given of processing of updating the metriccorrelation information 143 and the correlation coefficient information144. FIG. 16 is a flowchart for illustrating an example of theprocessing of updating the metric correlation information 143 and thecorrelation coefficient information 144 to be executed by the managementserver 100 in the first embodiment.

The management server 100 periodically executes processing describedbelow. The management server may execute the processing in a case wherereceiving a command from the user.

The correlation information generation module 123 of the managementserver 100 starts loop processing for a resource (Step S601).

Specifically, the correlation information generation module 123 selectsone resource from the configuration information 141. For example, amethod of selecting a resource in order from higher entries isconceivable.

Next, the correlation information generation module 123 executes therelated-resource identification processing (Step S602). Therelated-resource identification processing is the same as the processingillustrated in FIG. 11, and thus a description thereof is omitted here.

Next, the correlation information generation module 123 starts loopprocessing for a related resource (Step S603).

Specifically, the correlation information generation module 123 selectsone related resource from among related resources registered in therelated-resource list.

Next, the correlation information generation module 123 starts loopprocessing for a metric of the resource (Step S604).

Specifically, the correlation information generation module 123 selectsone metric from among metrics of the resource. In the followingdescription, the metric selected in Step S604 is referred to as a “firsttarget metric”.

Next, the correlation information generation module 123 starts loopprocessing for a metric of the related resource (Step S605).

Specifically, the correlation information generation module 123 selectsone metric from among metrics of the related resource. In the followingdescription, the metric selected in Step S605 is referred to as a“second target metric”.

Next, the correlation information generation module 123 instructs themetric value estimation module 125 to execute the estimated metric valuecalculation processing (Step S606). The correlation informationgeneration module 123 is in a standby state until the metric valueestimation module 125 outputs the estimated metric value of the firsttarget metric.

The flow of the estimated metric value calculation processing is thesame as the estimated metric value calculation processing described withreference to FIG. 14. However, in Step S501, the metric value estimationmodule 125 obtains time-series data on the metric value of the secondtarget metric. Further, in Step S502, the metric value estimation module125 uses the time-series data on the metric value of the second targetmetric to calculate time-series data on the estimated metric value ofthe first target metric.

Next, the correlation information generation module 123 executes thecorrelation analysis (Step S607).

Specifically, the correlation information generation module 123 executesthe correlation analysis that uses the time-series data on the metricvalue of the first target metric and the time-series data on theestimated metric value of the first target metric to calculate thecorrelation coefficient.

Next, the correlation information generation module 123 updates themetric correlation information 143 and the correlation coefficientinformation 144 (Step S608). Specifically, the following processing isexecuted.

In a case where the metric correlation information 143 and thecorrelation coefficient information 144 are not generated, thecorrelation information generation module 123 generates data in a matrixformat having the resource metric as its row and the related-resourcemetric as its column. The correlation information generation module 123sets identification information in a cell corresponding to a combinationof the first target metric and the second target metric. Further, thecorrelation information generation module 123 adds an entry to thecorrelation coefficient information 144, and sets values in thecorrelation coefficient ID 601 and the correlation coefficient 602 ofthe added entry.

In a case where the metric correlation information 143 and thecorrelation coefficient information 144 are generated, the correlationinformation generation module 123 determines whether identificationinformation is set in the cell corresponding to a combination of thefirst target metric and the second target metric.

In a case where identification information is not set in the cell, thecorrelation information generation module 123 sets identificationinformation in the cell. Further, the correlation information generationmodule 123 adds an entry to the correlation coefficient information 144,and sets values in the correlation coefficient ID 601 and thecorrelation coefficient 602 of the added entry.

In a case where identification information is set in the cell, thecorrelation information generation module 123 refers to the correlationcoefficient information 144, and searches for an entry corresponding tothe identification information set in the cell. The correlationinformation generation module 123 sets a value in the correlationcoefficient 602 of the retrieved entry. This concludes the descriptionof the processing of Step S608.

Next, the correlation information generation module 123 determineswhether the processing is complete for all the metrics of the relatedresource (Step S609).

In a case where it is determined that the processing is not complete forall the metrics of the related resource, the correlation informationgeneration module 123 returns to Step S605, and selects a next metric toexecute similar processing.

In a case where it is determined that the processing is not complete forall the metrics of the related resource, the correlation informationgeneration module 123 determines whether the processing is complete forall the metrics of the resource (Step S610).

In a case where it is determined that the processing is not complete forall the metrics of the resource, the correlation information generationmodule 123 returns to Step S604, and selects a next metric to executesimilar processing.

In a case where it is determined that the processing is complete for allthe metrics of the resource, the correlation information generationmodule 123 determines whether the processing is complete for all therelated resources (Step S611).

In a case where it is determined that the processing is complete for allthe related resources, the correlation information generation module 123returns to Step S603, and selects a next related resource to executesimilar processing.

In a case where it is determined that the processing is complete for allthe related resources, the correlation information generation module 123determines whether the processing is complete for all the resources(Step S612).

In a case where it is determined that the processing is not complete forall the resources, the correlation information generation module 123returns to Step S601, and selects a next related resource to executesimilar processing.

In a case where it is determined that the processing is complete for allthe resources, the correlation information generation module 123 endsthe processing.

As described above, according to the first embodiment, it is possible toquickly identify the bottleneck by performing analysis on narrowed-downmetrics highly correlated with the monitored metric of the monitoredresource. Further, the metric correlation information 143 and thecorrelation coefficient information 144 can be periodically updated tonarrow down metrics in consideration of the operation state of thesystem to be monitored.

The present invention is not limited to the above embodiment andincludes various modification examples. In addition, for example, theconfigurations of the above embodiment are described in detail so as todescribe the present invention comprehensibly. The present invention isnot necessarily limited to the embodiment that is provided with all ofthe configurations described. In addition, a part of each configurationof the embodiment may be removed, substituted, or added to otherconfigurations.

A part or the entirety of each of the above configurations, functions,processing units, processing means, and the like may be realized byhardware, such as by designing integrated circuits therefor. Inaddition, the present invention can be realized by program codes ofsoftware that realizes the functions of the embodiment. In this case, astorage medium on which the program codes are recorded is provided to acomputer, and a CPU that the computer is provided with reads the programcodes stored on the storage medium. In this case, the program codes readfrom the storage medium realize the functions of the above embodiment,and the program codes and the storage medium storing the program codesconstitute the present invention. Examples of such a storage medium usedfor supplying program codes include a flexible disk, a CD-ROM, aDVD-ROM, a hard disk, a solid state drive (SSD), an optical disc, amagneto-optical disc, a CD-R, a magnetic tape, a non-volatile memorycard, and a ROM.

The program codes that realize the functions written in the presentembodiment can be implemented by a wide range of programming andscripting languages such as assembler, C/C++, Perl, shell scripts, PHP,and Java (registered trademark).

It may also be possible that the program codes of the software thatrealizes the functions of the embodiment are stored on storing meanssuch as a hard disk or a memory of the computer or on a storage mediumsuch as a CD-RW or a CD-R by distributing the program codes through anetwork and that the CPU that the computer is provided with reads andexecutes the program codes stored on the storing means or on the storagemedium.

In the above embodiment, only control lines and information lines thatare considered as necessary for description are illustrated, and all thecontrol lines and information lines of a product are not necessarilyillustrated. All of the configurations of the embodiment may beconnected to each other.

What is claimed is:
 1. A computer configured to identify one or morebottleneck candidates and to manage a resource for which a metric beingan indicator for evaluating performance of the resource is to bemeasured, the computer comprising: a processor; a storage apparatuscoupled to the processor; and an interface coupled to the processor, thecomputer being coupled to, via the interface, a management target systemincluding a plurality of computation resources, the storage apparatusstoring correlation information for managing correlation coefficientsindicating degrees of correlation between metrics, and the computerbeing configured to: detect a change in performance of the managementtarget system to identify a bottleneck of the management target systembased on a metric value of a monitored metric of a monitored resource ofthe plurality of computation resources of the management target system;identify at least one related resource of the plurality of computationresources of the management target system having a coupling relationshipwith the monitored resource; identify at least one correlation metricthat is correlated with the monitored metric from among metrics of theat least one related resource based on the correlation information;identify a combination of the at least one related resource and the atleast one correlation metric as a bottleneck candidate; calculate anestimated metric value of the monitored metric through use of a metricvalue of the at least one correlation metric; execute statisticalanalysis that uses an actually measured metric value of the monitoredmetric and the estimated metric value of the monitored metric; determinewhether the at least one correlation metric influences the monitoredmetric based on a result of the statistical analysis; and generatenotification information for notifying the bottleneck candidate, andoutput the notification information comprising displaying the bottleneckcandidate in a bottleneck candidate display field.
 2. The computeraccording to claim 1, wherein the storage apparatus stores functioninformation for managing a conversion function that converts the metricvalue of the at least one correlation metric into the metric value ofthe monitored metric, and wherein the computer is configured to: referto the function information to identify a first conversion function,which converts the metric value of the at least one correlation metricinto the metric value of the monitored metric for calculating theestimated metric value of the monitored metric; and substitute themetric value of the at least one correlation metric into the firstconversion function to calculate the estimated metric value of themonitored metric.
 3. The computer according to claim 2, wherein thecomputer is configured to: calculate a first correlation coefficient byexecuting correlation analysis for correlation between the actuallymeasured metric value of the monitored metric and the estimated metricvalue of the monitored metric; identify the at least one correlationmetric that influences the monitored metric based on a result ofcomparison between the first correlation coefficient and a thresholdvalue; and identify a combination of the at least one related resourceand the identified at least one correlation metric as the bottleneckcandidate.
 4. The computer according to claim 2, wherein the computer isconfigured to: calculate a first correlation coefficient by executingcorrelation analysis for correlation between the actually measuredmetric value of the monitored metric and the estimated metric value ofthe monitored metric; obtain a second correlation coefficient forcorrelation between the metric value of the monitored metric and themetric value of the at least one correlation metric; identify the atleast one correlation metric that influences the monitored metric basedon a difference between the first correlation coefficient and the secondcorrelation coefficient; and identify a combination of the at least onerelated resource and the identified at least one correlation metric asthe bottleneck candidate.
 5. A bottleneck identification method to beexecuted by a computer configured to identify one or more bottleneckcandidates and manage a resource for which a metric being an indicatorfor evaluating performance of the resource is to be measured, thecomputer including: a processor; a storage apparatus coupled to theprocessor; and an interface coupled to the processor, the computer beingcoupled to, via the interface, a management target system including aplurality of computation resources, the storage apparatus storingcorrelation information for managing correlation coefficients indicatingdegrees of correlation between metrics, the bottleneck identificationmethod including: a first step of detecting, by the computer, a changein performance of the management target system to identify a bottleneckof the management target system based on a metric value of a monitoredmetric of a monitored resource of the plurality of computation resourcesof the management target system; a second step of identifying, by thecomputer, at least one related resource of the plurality of computationresources of the management target system having a coupling relationshipwith the monitored resource; a third step of identifying, by thecomputer, at least one correlation metric that is correlated with themonitored metric from among metrics of the at least one related resourcebased on the correlation information; a fourth step of identifying, bythe computer, a combination of the at least one related resource and theat least one correlation metric as a bottleneck candidate; and a fifthstep of generating, by the computer, notification information fornotifying of the bottleneck candidate, and outputting the notificationinformation comprising displaying the bottleneck candidate in abottleneck candidate display field, wherein the fourth step includes: asixth step of calculating, by the computer, an estimated metric value ofthe monitored metric through use of a metric value of the at least onecorrelation metric: a seventh step of executing, by the computer,statistical analysis that uses an actually measured metric value of themonitored metric and the estimated metric value of the monitored metric;and an eighth step of determining, by the computer, whether the at leastone correlation metric influences the monitored metric based on a resultof the statistical analysis.
 6. The bottleneck identification methodaccording to claim 5, wherein the storage apparatus stores functioninformation for managing a conversion function that converts the metricvalue of the at least one correlation metric into the metric value ofthe monitored metric, and wherein the sixth step includes: referring, bythe computer, to the function information to identify a first conversionfunction, which converts the metric value of the at least onecorrelation metric into the metric value of the monitored metric forcalculating the estimated metric value of the monitored metric; andsubstituting, by the computer, the metric value of the at least onecorrelation metric into the first conversion function to calculate theestimated metric value of the monitored metric.
 7. The bottleneckidentification method according to claim 6, wherein the seventh stepincludes calculating, by the computer, a first correlation coefficientby executing correlation analysis for correlation between the actuallymeasured metric value of the monitored metric and the estimated metricvalue of the monitored metric, and wherein the eighth step includes:identifying, by the computer, the at least one correlation metric thatinfluences the monitored metric based on a result of comparison betweenthe first correlation coefficient and a threshold value; andidentifying, by the computer, a combination of the at least one relatedresource and the identified at least one correlation metric as thebottleneck candidate.
 8. The bottleneck identification method accordingto claim 6, wherein the seventh step includes calculating, by thecomputer, a first correlation coefficient by executing correlationanalysis for correlation between the actually measured metric value ofthe monitored metric and the estimated metric value of the monitoredmetric, and wherein the eighth step includes: obtaining, by thecomputer, a second correlation coefficient for correlation between themetric value of the monitored metric and the metric value of the atleast one correlation metric; identifying, by the computer, the at leastone correlation metric that influences the monitored metric based on adifference between the first correlation coefficient and the secondcorrelation coefficient; and identifying, by the computer, a combinationof the at least one related resource and the identified at least onecorrelation metric as the bottleneck candidate.
 9. A non-transitorycomputer readable storage medium having stored thereon a program forcausing a computer to identify one or more bottleneck candidates andmanage a resource for which a metric being an indicator for evaluatingperformance of the resource is to be measured, the computer beingcoupled to a management target system including a plurality ofcomputation resources, an storage apparatus storing correlationinformation for managing correlation coefficients indicating degrees ofcorrelation between metrics, the program causing the computer toexecute: a first procedure of detecting a change in performance of themanagement target system to identify a bottleneck of the managementtarget system based on a metric value of a monitored metric of amonitored resource of the plurality of computation resources of themanagement target system; a second procedure of identifying at least onerelated resource of the plurality of computation resources of themanagement target system having a coupling relationship with themonitored resource; a third procedure of identifying at least onecorrelation metric that is correlated with the monitored metric fromamong metrics of the at least one related resource based on thecorrelation information; a fourth procedure of identifying a combinationof the at least one related resource and the at least one correlationmetric as a bottleneck candidate; and a fifth procedure of generatingnotification information for notifying of the bottleneck candidate, andoutputting the notification information comprising displaying thebottleneck candidate in a bottleneck candidate display field, whereinthe fourth procedure includes: a sixth procedure of calculating anestimated metric value of the monitored metric through use of a metricvalue of the at least one correlation metric; a seventh procedure ofexecuting statistical analysis that uses an actually measured metricvalue of the monitored metric and the estimated metric value of themonitored metric; and an eighth procedure of determining whether the atleast one correlation metric influences the monitored metric based on aresult of the statistical analysis.
 10. The non-transitory computerreadable storage medium having the program stored thereon according toclaim 9, wherein the computer stores function information for managing aconversion function that converts the metric value of the at least onecorrelation metric into the metric value of the monitored metric, andwherein the sixth procedure includes the procedures of: referring to thefunction information to identify a first conversion function, whichconverts the metric value of the at least one correlation metric intothe metric value of the monitored metric for calculating the estimatedmetric value of the monitored metric; and substituting the metric valueof the at least one correlation metric into the first conversionfunction to calculate the estimated metric value of the monitoredmetric.
 11. The non-transitory computer readable storage medium havingthe program stored thereon according to claim 10, wherein the seventhprocedure includes a procedure of calculating a first correlationcoefficient by executing correlation analysis for correlation betweenthe actually measured metric value of the monitored metric and theestimated metric value of the monitored metric, and wherein the eighthprocedure includes the procedures of: identifying the at least onecorrelation metric that influences the monitored metric based on aresult of comparison between the first correlation coefficient and athreshold value; and identifying a combination of the at least onerelated resource and the identified at least one correlation metric asthe bottleneck candidate.
 12. The non-transitory computer readablestorage medium having the program stored thereon according to claim 10,wherein the seventh procedure includes a procedure of calculating afirst correlation coefficient by executing correlation analysis forcorrelation between the actually measured metric value of the monitoredmetric and the estimated metric value of the monitored metric, andwherein the eighth procedure includes the procedures of: obtaining asecond correlation coefficient for correlation between the metric valueof the monitored metric and the metric value of the at least onecorrelation metric; identifying the at least one correlation metric thatinfluences the monitored metric based on a difference between the firstcorrelation coefficient and the second correlation coefficient; andidentifying a combination of the at least one related resource and theidentified at least one correlation metric as the bottleneck candidate.